Technology Transfer Project
SENECA: Semi-automatic neuron reconstruction in CATMAID
Technology Transfer Contract
The field of Connectomics (mapping neural circuits) emerged in recent years with the development of high throughput imaging methods for the recording of brain tissue. Electron Microscopy (EM) allows resolving neural circuits at synaptic level and thus has great potential to push forward our understanding of nervous systems.
In the last decade, automatic neuron segmentation has became an active part of computer vision research, spanning several subfields of applied computer science. State-of-the-art techniques combine low-level image processing, deep-learning methods, structured output prediction, and discrete-optimization based approaches for image segmentation.
Although significant progress has been made already, the goal of getting automated segmentations accurate enough to answer biological questions has not been reached yet. This project aims at improving the state of the art and developing new methods for this interesting and challenging goal. The project will include working on following problems:
• investigation of novel segmentation methods for EM volumes
• incorporation of biological priors
• learning of model parameters from sparse human annotations
• development of distributed systems for large-scale reconstruction
This project is supported by Howard Hughes Medical Institute, Janelia Research Campus, USA.
J. Funke, J. Klein, F. Moreno-Noguer, A. Cardona and M. Cook. Structured learning of assignment models for neuron reconstruction to minimize topological errors, 13th IEEE International Symposium on Biomedical Imaging, 2016, Prague, pp. 607-611.
J. Buhmann, S. Gerhard, M. Cook and J. Funke. Tracking of microtubules in anisotropic volumes of neural tissue, 13th IEEE International Symposium on Biomedical Imaging, 2016, Prague, pp. 326-329.